Exploration of lead compounds with desired properties typically comprises high throughput or virtual screening. These methods are slow, costly, and ineffective.
In high throughput screening, chemical compounds from a compound library are tested. However, compound libraries are huge and most of the candidates are not eligible to be selected as a hit compound. To minimize costs associated with this complicated approach, some screening methods utilize in silico methods, known as virtual screening. However, available virtual screening methods require tremendous computational power and they can be algorithmically poor and time consuming.
Further, current hit-to-lead exploration primarily comprises exhaustive screening from vast lists of chemical compound candidates. This approach relies on the expectation and hope that a compound with a set of desired properties will be found within existing lists of chemical compounds. Further, even when current screening methods successfully find lead compounds, it does not mean that these lead compounds can be used as drugs. It is not rare for candidate compounds to fail at later stage of clinical trial. One of the major reasons of failure is toxicity or side effects that are not revealed until experiments with animals or humans. Finally, these exploration models are slow and costly.
Because of the inefficiencies and limitations of existing methods, there is a need for drug design methods that directly generate candidate chemical compounds having the desired set of properties, such as binding to a target protein. There is yet another need for generating candidate chemical compounds lacking toxicity or side effects. There is a final need for predicting how candidate chemical compounds would interact off-target and/or with other targets.